Big Data Analytics for Healthcare

Section I. Theories and Concepts of Big Data Analytics in Healthcare1. Big data analytics in healthcare: Theory, tools, techniques and its applications2. Driving impact through big data utilization and analytics in the context of a learning health system3. Classification of medical big data: A review of systematic analysis of medical big data in real time setup4. Towards big data framework in government public open data (GPOD) for healthSection II. Big Medical Data: Techniques, Managements, and Applications5. Big data analytics techniques for healthcare6. Big data analytics in precision medicine7. Recent advances in processing, interpreting, and managing biological data for therapeutic intervention of human infectious disease8. Big data analytics for health: A comprehensive review of techniques and applicationsSection III. Diagnosis and Treatment: Big Data Analytical Techniques, Datasets, Life Cycles, Managements and Applications for Diagnosis and Treatment9. Recent applications of data mining in medical diagnosis and prediction10. Big medical data analytics for diagnosis11. Big data analytics and radiomics to discover diagnostics on different cancer types12. Big medical data, cloud computing and artificial intelligence for improving diagnosis in healthcareSection IV. Prediction: Big Data Analytical Techniques, Datasets, Life Cycles, Managements and Applications for Prediction13. Use of artificial intelligence for predicting infectious disease14. Hospital data analytics system for tracking and predicting obese patients' lifestyle habits15. Predictions on diabetic patient datasets using big data analytics and machine learning techniques16. Skin cancer prediction using big data analytics and AI techniquesSection V. Big Medical Fake News Analytics for Preventing Medical Misinformation and Myths17. COVID-19 fake news analytics from social media using topic modeling and clustering18. Big medical data mining system (BigMed) for the detection and classification of COVID-19 misinformation Section VI. Challenges and Future of Big Data in Healthcare19. Privacy security risks of big data processing in healthcare20. Opportunities and challenges in healthcare with the management of big biomedical data21. Future direction for healthcare based on big data analyticsSection VII. Case Studies of Big Data in Healthcare Arena22. Big data in orthopedics: Between hypes and hopes23. Predicting onset (type-2) of diabetes from medical records using binary class classification24. Screening programs incorporating big data analytics

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Algorithms; Analytical dashboard; Analytics; Artificial intelligence; Artificial Intelligence; Autism; Big data analytics; Big Data analytics; Big data challenges; Big data framework; Big data in healthcare; Big data; Big Data; Big medical data; Biomedical data; Biomedical; Cancer; Classification; Clinical decision support systems; Cloud computing; Cloud services; Clustering; Computational challenges; Convolutional neural network (CNN)Deep learning; COVID-19Deep learning; COVID-19Fake news analytics; COVID-19Fake news; Covid-19Hadoop; Data analysis; Data analytics in health care; Data management; Data mining; Data science engineering; Data warehouses; Decision tree; Decision; Diabetes; Diabetic; Diagnosis; Disease prediction; EHR; EHRs; Electronic health records; Epidemiological registries; Epidemiology; Government public open data; Habit change; Health sector; Health technologies; Health; Healthcare security; healthcare; Healthcare; High-throughput technologies; Hospital hubs; Hospital information systems; Impact; InceptionResNetV2Melanoma; Infectious disease prediction; Infectious diseases; Intelligent medical diagnosis systems; Internet of things; Latent Dirichlet Allocation (LDA)Misinformation; Learning Health System; Learning healthcare system; Lifestyle habit; Logistic regression; Machine learning-based e-medicine; Machine learning; Medical apps; Medical diagnosis; Medical imaging; Medical intelligence; MelConvo2d; Misinformation; MobileNet SSD; Natural language processing; Nonconventional data streams; Omics; Open data; Orthopedics; Precision medicine; Prediction model; Prediction; Privacy measures; Privacy security risk; Public data for health; PySpark; Radiomics analysis; Radiomics; Remote patient monitoring; Rules; Skin cancer; Social media analytics; Surveillance; Sustainability; Telemedicine; Topic modeling; VGG19Wearable technologies